An Analytical Study of Traffic Accidents in Connecticut, USA Using Python

Amidst the growing traffic networks, the need for nuanced perspectives on traffic safety becomes increasingly apparent. Traffic patterns are affected by different factors in different parts of the world. This study focuses on the state of Connecticut, conducting a careful analysis of road accidents to discern patterns and trends affecting traffic accidents. Using a comprehensive dataset from the Connecticut Department of Transportation, we use Python and its data analytics libraries to unveil the dynamics shaping road safety in the state. We concentrated on five different aspects of a car accident: frequency of accidents, spatial distribution of accidents in the different towns, the time of day of the accident, the distribution of vehicle colors, and age demographics among vehicle proprietors. Our findings show that some of these factors in our analysis are unique to the state of Connecticut while other show a following of general trends in the USA and the world. By showing the specific dynamics of the state, we lay the groundwork for state-targeted methods and strategies to minimize traffic accidents. This would potentially lead to a safer traffic landscape in the future.